(1) Field of the Invention
This invention relates to code-division-multiple-access (CDMA) wireless communication systems, in which multiple base stations each transmit a respective CDMA signal. In particular, the present invention relates to a methods and receivers for recovering data from the CDMA signals transmitted by the multiple base stations.
(2) Description of the Related Art
CDMA is a crucial technology for third-generation (3G) wireless communication systems. A known CDMA system, which is illustrated in FIG. 1, includes base stations 1, 2 each defining a “cell”, that is an area surrounding the base station in which transmissions from the base station may be received. Users are provided with mobile communications devices 3, 4 (e.g. mobile phones), which communicate with the base station associated with the cell in which they are located, and in particular receive from that base station a “forward link” wireless CDMA signal.
FIG. 2 shows communication from base station 1 transmitting a CDMA signal to a certain mobile device 3. Labelling the K mobile devices 3 in the cell of base station 1 by integer index k=1, . . . K, the CDMA signal is generated using a spread code for each user, denoted by Ck1, and a scrambling code for base station 1, denoted spm1. Each Ck1 signal varies at time increments called “chips” and has a period N chips (a “symbol”), and the spreading codes are orthogonal in the sense that the sum over the chips of one symbol of the product of the spreading signals for two different receivers is zero.
A conventional receiver provided in device 3 can receive data intended for that device 3 very well over ideal channels for which all signals generated by users are orthogonal to each other. However, as shown schematically in FIG. 2, in a real environment the signal from base station 1 received by the receiver of a mobile device 3 is distorted seriously.
In other known systems, a RAKE receiver is used to handle the multipath problem [1,2]. The RAKE receiver combines several path components for one user to increase the signal to noise ratio (SNR). Its performance is optimal when there is a single-user. However, when the received signals include signals from other users, inter-path interference will destroy the orthogonality between users, and cause multiple-access interference (MAI). Moreover, if a receiver is near the edge of a cell in the cellular CDMA systems, its signal is subject to interference not only from intra-cell MAI, but also from MAI due to other cells. The MAI will degrade the performance of the receiver seriously.
Some MAI suppression methods have been published in the literature [3,4,5,6]. These methods are complex, or cannot handle a long scrambling code. Hence, they are not suitable for use in a CDMA forward link of a mobile phone system.
Since the signals of users in the forward link of a cell pass through the same channel before they reach the receiver, it is possible for us to adopt channel equalisation to recover the orthogonality between signals so as to suppress MAI and improve the performance of the receiver. In recent papers [7,8,9,10,11], methods for MAI suppression in a CDMA forward link using channel equalisation have been presented. They can handle an aperiodic scrambling code, and the complexity is low. However, these methods consider only single cell multipath channel equalization, and hence cannot suppress MAI due to other cells. Their performance will degrade when a mobile device is near the edge of one cell and subject to MAI from one or more other cells simultaneously. Besides, all of the methods in [7,8,9,10,11] adopt three independent steps to complete the total function, namely channel equalization, descrambling/despreading and decision. Since the adaptive loop includes only a channel equalizer, a channel estimation process must be completed before the channel equalization. In addition, many existing channel estimation approaches require a knowledge the order of the multipath channel, which is unknown in a real environment, and can be only estimated approximately. The estimation errors in channel order and weights will be brought into the equalizer and influence the performance of the equalizer. Furthermore, as the channel parameters change, these methods need to adjust the channel estimation and channel equalization repeatedly to match these changes. Such approaches waste time and lack robustness.